Systems and methods for searching images within a database of images, according to the content of the images are known in the art. A user inputs an image query and a visual characterization for searching a similar image (i.e., similar to the image query) within the database of images. A system for blending a series of panoramic images into a single panorama is also known in the art. The system computes a Laplacian pyramid and a Gaussian pyramid for each of the images. The system blends the Laplacian pyramid and the Gaussian pyramid in the overlapping edge area of each pair of adjacent images out of the series of panoramic images.
U.S. Pat. No. 5,893,095 issued to Jain et al., and entitled “Similarity Engine for Content-Based Retrieval of Images”, is directed to a search engine for retrieving images according to their content. The search engine includes a set of primitives, a registration interface, and a comparator. The primitives are coupled with the registration interface and with the comparator.
Each of the primitives includes at least one extraction function, capable of extracting attributes from a visual object and capable of determining similarity between visual objects. The extraction functions of the primitives can extract general attributes (e.g., color, shape, and texture of image), or domain specific attributes (e.g., attributes relevant for cancer cell identification, or face recognition). The primitives express an image as a compact semantic representation of the visual characteristics of the image. The registration interface registers the primitives. The comparator applies the extraction functions of the primitives for comparison of objects.
A user defines at least one primitive. The registration interface registers the primitive and stores the primitive in a look up table. The user inputs an image query. The primitive extracts a feature vector from the image query. The comparator compares the feature vector of the image query with a feature vector of each of a plurality of images stored on an image database, and assigns a similarity score to each of the plurality of images. The search engine retrieves the image with the highest similarity score.
U.S. Pat. No. 6,532,301 B1 issued to Krumm et al., and entitled “Object Recognition with Occurrence Histograms”, is directed to a method for finding an object being sought in a search engine. The object finding method includes the following steps capturing model images of the object, generating a plurality of search windows, computing a Co-occurrence Histogram (CH) for each search window, assessing a degree of similarity between each model image CH and each of the search window CH's, and designating a search window.
The model images of the object are captured from a plurality of different viewpoints. Ideally, these different viewpoints are spaced at roughly equal angles from each other around the object. The search windows are generated from a search image (i.e., the image to be searched for the modeled object). The search windows are generated by cordoning the search image into a series of preferably equal sized sub-images (i.e., search windows). These search windows preferably overlap both side-to-side and up-and-down.
A model image CH is computed by generating counts of every pair of pixels whose pixels exhibit colors that fall within the same combination of a series of pixel color ranges and which are separated by a distance falling within the same one of a series of distance ranges. A degree of similarity is assessed by comparing each model image CH and each of the search window CH's. A search window is designated as potentially containing the object being sought, if it is associated with a search window CH, having a degree of similarity to one of the model image CH's, which exceeds a prescribed search threshold.
U.S. Pat. No. 6,359,617 B1 issued to Xiong, and entitled “Blending Arbitrary Overlaying Images into Panoramas”, is directed to a method for generating panoramas from two dimensional images. The panoramas generating method includes the steps of storing at least two rectilinear images in a computer memory, constructing a Laplacian pyramid, determining the coarsest resolution level, constructing a Gaussian pyramid, combining the Laplacian pyramid and the Gaussian pyramid.
At least two rectilinear images are stored in a computer memory. A Laplacian pyramid (i.e., an image compression technique in which each level contains less pixels and occupies less space) is constructed for the overlap region of each of the two images. The coarsest resolution level, for blending the two images, is determined according to the inertial tensor of the two images. A Gaussian pyramid is constructed for the overlap region of each of the two images. The Laplacian pyramid and the Gaussian pyramid are combined for blending the two images in the overlap region of the two images.
An article by Marius Leordeanu and Martial Hebert of The Robotics Institute at Carnegie Mellon University, Pittsburgh, entitled “A Spectral Technique for Correspondence Problems Using Pairwise Constraints”, is directed to a method for finding consistent correspondence between two sets of feature points. The correspondence method disclosed in the article includes the steps of computing an affinity measure between the descriptors of every assignment of a feature point of the first set with a feature point of the second set, computing an affinity measure of the compatibility of every pair of assignments of feature points of the two sets, constructing an affinity matrix, computing the principal eigenvector of the affinity matrix, construct the solution vector.
In the first step, an affinity measure between the descriptors of every assignment of a feature point of the first set with a feature point of the second set is computed. For example, two sets including two points each (i.e., a first set of points one and two and a second set of points three and four) will result in four such affinity measures between descriptors, points one and three, points one and four, points two and three, and points two and four.
In the second step, an affinity measure of the compatibility of every pair of assignments of feature points of the two sets is computed. In the example herein above the affinity measure of the compatibility of assignment of points one and three with assignment of points two and four is computed. In the third step, an affinity matrix is constructed, such that the affinity matrix includes all the affinity measures of the compatibility of pairs of assignments.
In the forth step, the principal eigenvector of the affinity matrix is determined. In the fifth step, solution vector is computed. The assignment corresponding to the highest magnitude entry within the principal eigenvector is determined. The entry of the solution vector, corresponding to the highest magnitude entry of the principal eigenvector is denoted as one. the assignment corresponding to the highest magnitude is deleted from the set of the assignments. The procedures of the fourth and fifth steps are repeated until the highest magnitude entry equals to the zero.